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A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases.
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Stable motion and distributed topology control for multi-agent systems with directed interactions
In this paper, we study stable coordination in multi- agent systems with directed interactions, and apply the results for distributed topology control. Our main contribution is to extend the well-known potential-based control framework orig- inally introduced for undirected networks to the case of net- works modeled by a directed graph. Regardless of the particular objective to be achieved, potential-based control for undirected graphs is intrinsically stable. Briefly, this can be explained by the positive semidefiniteness of the graph Laplacian induced by the symmetric nature of the interactions. Unfortunately, this energy finiteness guarantee no longer holds when a multi-agent system lacks symmetry in pairwise interactions. In this context, our contribution is twofold: i) we formalize stable coordination of multi-agent systems on directed graphs, demonstrating the graph structures that induce stability for a broad class of coordination objectives; and ii) we design a topology control mechanism based on a distributed eigenvalue estimation algorithm to enforce Lyapunov energy finiteness over the derived class of stable graphs. Simulation results demonstrate a multi-agent system on a directed graph performing topology control and collision avoidance, corroborating the theoretical findings.
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- Award ID(s):
- 1657235
- PAR ID:
- 10055505
- Date Published:
- Journal Name:
- Decision and Control (CDC), 2017 IEEE 56th Annual Conference on
- Page Range / eLocation ID:
- 3455 to 3460
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CDC.2017.8264165
